Performance Engineering and Cost Optimization Questions

Engineering practices and trade offs for meeting performance objectives while controlling operational cost. Topics include setting latency and throughput targets and latency budgets; benchmarking profiling and tuning across application database and infrastructure layers; memory compute serialization and batching optimizations; asynchronous processing and workload shaping; capacity estimation and right sizing for compute and storage to reduce cost; understanding cost drivers in cloud environments including network egress and storage tiering; trade offs between real time and batch processing; and monitoring to detect and prevent performance regressions. Candidates should describe measurement driven approaches to optimization and be able to justify trade offs between cost complexity and user experience.

HardSystem Design

0 practiced

Design a monitoring and alerting system that detects subtle regressions (e.g., 10% p95 increase) in data pipelines and can trigger automated remediation. Define the metrics (p50/p95/p99, throughput, input lag, error-rate), baseline approach, anomaly detection algorithm, and safe automated remediations (scale up, restart job, revert release). Discuss rollback and alert noise precautions.

MediumTechnical

0 practiced

Estimate capacity to support 100k events/sec for a streaming ingestion system where average event size is 1 KB. Provide calculations for network bandwidth, daily storage, and approximate CPU/core requirements for processing assuming 2x replication for durability. State your assumptions and include a safety margin.

MediumSystem Design

0 practiced

Design a monitoring and alerting approach to detect performance regressions in production data pipelines. Specify which metrics to collect (latency quantiles, throughput, input lag, error rates), how to baseline and detect anomalies, and how to automate short-term remediation (auto-scaling, circuit-breakers) while avoiding alert fatigue.

EasyTechnical

0 practiced

Explain backpressure in stream-processing systems and why it matters for system stability and cost. Provide two strategies to handle backpressure in a pipeline: one that favors data durability and one that favors availability, and explain trade-offs.

MediumTechnical

0 practiced

You want to detect performance regressions automatically in CI for critical ETL jobs. Propose a CI-based performance testing strategy: workload generation, stable test dataset, metrics to assert (p95/p99, memory), thresholds, and how to avoid flaky failures from environmental noise.

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